Case and Activity Identification for Mining Process Models from Middleware

Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider

Template-free synthesis of hybrid silica nanoparticle with functionalized mesostructure for efficient methylene blue removal

A simple one-pot synthesis process for functionalized mesostructured silica nanoparticles (MSNP) is reported. The novel process demonstrated the possibility to achieve MSNP with a surface area up to 501 m2.g−1 using a phosphonate based nonsilane precursor such as N, N´-bis[4,6-bis(diethylphosphono)-1,3,5-triazin-yl]-1,2-diaminoethane (ED). MSNP obtained by using 20 mol% of ED achieved a surface area of 80 m2.g−1 and increasing the ED content to 30 mol% resulted in a surface area of 501 m2.g−1. Zeta potential of novel MSNPs (−65.5 and 70.0 mV) were much higher than the nanoparticle (NP) prepared from only TEOS (−49 mV), indicating the presence of a large number of –SiOH and phosphonic acid surface functional groups, as confirmed by Fourier-transform infrared spectroscopy (FT-IR) and Nuclear magnetic resonance (NMR) analysis. The functionalized MSNPs were used as an adsorbent for the removal of cationic pollutants like methylene blue (MB). The MSNP with the highest porosity displayed favorable MB adsorption behavior with ~380 mg.g−1 of MB adsorption capacity. Facile regeneration in an acidic medium (~pH 4.5) with easy recyclability (10 cycles) confirmed the practical applicability of this novel functionalized MSNPs

Simulated logs

There are 21 event logs generated using the 21 process model in https://figshare.com/articles/dataset/_/20732095. The logs are available in mxml and csv formats  </p

Exceeding Pinch limits by process configuration of an existing modern crude oil distillation unit – A case study from refining industry

Crude Distillation Unit (CDU) represents significant challenge for retrofitting and energy optimisation as the most energy intensive consumer in a conventional crude oil refinery. Pinch Technology and its based-methodologies are found primary keys for decades to energy savings in refining industries for a range of common economic-based and environmental objectives or applications. Typical benefits in energy savings are reported within 20–40% of original designs. However, such savings are limited and questioned when modern refiners are dealt with. The current paper addresses the revamping of a modern refinery exhibiting an existing high energy efficiency (≈93%). This implies the maximum potential energy savings would only be 7% at current process conditions. The present research proposes an algorithm that tackles energy recovery of modern refiners, enabling additional savings beyond the energy targets set by the existing process. The algorithm starts by process simulation and validation against real plant data, followed by a network optimisation, e.g. stream splitting, to reach the energy targets set by Pinch Analysis. The energy targets are then moved to another lower level by performing potential process modifications to reduce the energy consumption further. Results showed that the current modern refinery unit could reach its energy targets by stream splitting modifications with hot energy savings of 2.69 MW. Process modifications resulted in additional energy savings of 31.3% beyond the current level of the existing plant alongside less than a year of payback period for estimated capital investment. An environmental assessment is performed, and comparable reductions were obtained with respect to greenhouse gas, with reduction in CO2 emissions by 45.1%. The proposed retrofit methodology is applicable to minimising energy consumptions of refiners including modern units to achieve energy levels beyond energy targets by new process modifications

Event log reconstruction using autoencoders

Poor quality of process event logs prevents high quality business process analysis and improvement. Process event logs quality decreases because of missing attribute values or after incorrect or irrelevant attribute values are identified and removed. Reconstructing a correct value for these missing attributes is likely to increase the quality of event log-based process analyses. Traditional statistical reconstruction methods work poorly with event logs, because of the complex interrelations among attributes, events and cases. Machine learning approaches appear more suitable in this context, since they can learn complex models of event logs through training. This paper proposes a method for reconstructing missing attribute values in event logs based on the use of autoencoders. Autoencoders are a class of feed-forward neural networks that reconstruct their own input after having learnt a model of its latent distribution. They suit problems of unsupervised learning, such as the one considered in this paper. When reconstructing missing attribute values in an event log, in fact, one cannot assume that a training set with true labels is available for model training. The proposed method is evaluated on two real event logs against baseline methods commonly used in the literature for imputing missing values in large datasets

Bot log mining: Using logs from robotic process automation for process mining

Robotic Process Automation (RPA) is an emerging technology for automating tasks using bots that can mimic human actions on computer systems. Most existing research focuses on the earlier phases of RPA implementations, e.g. the discovery of tasks that are suitable for automation. To detect exceptions and explore opportunities for bot and process redesign, historical data from RPA-enabled processes in the form of bot logs or process logs can be utilized. However, the isolated use of bot logs or process logs provides only limited insights and not a good understanding of an overall process. Therefore, we develop an approach that merges bot logs with process logs for process mining. A merged log enables an integrated view on the role and effects of bots in an RPA-enabled process. We first develop an integrated data model describing the structure and relation of bots and business processes. We then specify and instantiate a ‘bot log parser’ translating bot logs of three leading RPA vendors into the XES format. Further, we develop the ‘log merger’ functionality that merges bot logs with logs of the underlying business processes. We further introduce process mining measures allowing the analysis of a merged lo

A probabilistic approach to event-case correlation for process mining

Process mining aims to understand the actual behavior and performance of business processes from event logs recorded by IT systems. A key requirement is that every event in the log must be associated with a unique case identifier (e.g., the order ID in an order-to-cash process). In reality, however, this case ID may not always be present, especially when logs are acquired from different systems or when such systems have not been explicitly designed to offer process-tracking capabilities. Existing techniques for correlating events have worked with assumptions to make the problem tractable: some assume the generative processes to be acyclic while others require heuristic information or user input. In this paper, we lift these assumptions by presenting a novel technique called EC-SA based on probabilistic optimization. Given as input a sequence of timestamped events (the log without case IDs) and a process model describing the underlying business process, our approach returns an event log in which every event is mapped to a case identifier. The approach minimises the misalignment between the generated log and the input process model, and the variance between activity durations across cases. The experiments conducted on a variety of real-life datasets show the advantages of our approach over the state of the art